PORTFOLIO PROJECT 3. MARKET SEGMENTATION.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Online Retail Market Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9313eeaf-b9c6-456f-9a43-619de6a3d21c",
   "metadata": {},
   "source": [
    "### Data Source: UCI Machine Learning Repository"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23671a4-870f-4387-8219-792b043bc343",
   "metadata": {},
   "source": [
    "Market segmentation (or customer segmentation) are techniques to split customers into clusters based on similarities to get a sense of their behavior. In this notebook, we are going to analyze patterns in the Online Retail Data Set from the UCI Machine Learning Repository. \n",
    "\n",
    "As UCI specifies about their dataset:\n",
    "\n",
    "\"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\"\n",
    "\n",
    "Columns description\n",
    "\n",
    "1. InvoiceNo: A unique identifier for the invoice. An invoice number shared across rows means\n",
    "that those transactions were performed in a single invoice (multiple purchases).\n",
    "    \n",
    "2. StockCode: Identifier for items contained in an invoice.\n",
    "    \n",
    "3. Description: Textual description of each of the stock items.\n",
    "    \n",
    "4. Quantity: The quantity of the item purchased.\n",
    "    \n",
    "5. InvoiceDate: Date of purchase.\n",
    "    \n",
    "6. UnitPrice: Value of each item.\n",
    "    \n",
    "7. CustomerID: Identifier for customer making the purchase.\n",
    "    \n",
    "8. Country: Country of customer.\n",
    "\n",
    "Objective\n",
    "\n",
    "which type of customers are loyal to us.\n",
    "\n",
    "Get the market segmentation to better increase outcomes of marketing campaigns\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e747b55e-bac3-4b4a-86ac-7139090f3b5f",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd287ce7-8531-4e28-980b-1178cefcb943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Structure\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sklearn\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "\n",
    "# Others\n",
    "sns.set(style='darkgrid')\n",
    "sns.set_theme()\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f5ba3f-0727-4c71-b020-d0586335d292",
   "metadata": {},
   "source": [
    "## Loading The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7deb189c-cc0d-41b9-9ee7-991c505d0fb0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv(r\"C:\\Users\\Henry Oguns\\Desktop\\Data Science Portfolio\\datasets\\Online Retail.csv\")\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "14b8d4ec-55a2-4431-b96e-3697407ad778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed513eb9-8cf5-4180-a57a-a45db797a32c",
   "metadata": {},
   "source": [
    "The dataset contains 541909 oservations and 8 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19a9be3a-6e4b-47fe-944e-35a010d34897",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Understandin the data type of each columns\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7712fc6e-033f-4177-8a4d-d8ae7f339216",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the Invoice Date column to a date time data type\n",
    "\n",
    "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d903081c-61e0-4487-8b6b-6181d390bf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5268"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the duplicates in the dataset and droppping them \n",
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb426f4b-4a1f-4a89-83fd-7a7e4f55804a",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44b33de5-c965-460d-b207-1bf1bf6f47b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135037\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values in the dataset\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2cad8be-5730-41a1-b8b2-9f3087b2ec17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling the NAN values in the Description column with 'No Description'\n",
    "\n",
    "df['Description'] = df['Description'].fillna('No Description')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "edcfcc1d-78a0-4bc0-a7f1-7e2b2cc738a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "601ecc57-8703-46ec-964d-e401a2b20b2f",
   "metadata": {},
   "source": [
    "Not knowing the Cutomer ID of the particular column would prevent the accuracy customer segmentation using this dataset. Also  a decision to drop this rows out of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38505374-d592-4175-a750-9771a5c28a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401604, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "id": "f82d1d3d-cec4-4c82-ad84-630bb2492bfe",
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   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>401604.000000</td>\n",
       "      <td>401604</td>\n",
       "      <td>401604.000000</td>\n",
       "      <td>401604.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.183273</td>\n",
       "      <td>2011-07-10 12:08:23.848567552</td>\n",
       "      <td>3.474064</td>\n",
       "      <td>15281.160818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2011-04-06 15:02:00</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13939.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2011-07-29 15:40:00</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>15145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011-10-20 11:58:30</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>16784.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>250.283037</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.764035</td>\n",
       "      <td>1714.006089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "            Quantity                    InvoiceDate      UnitPrice  \\\n",
       "count  401604.000000                         401604  401604.000000   \n",
       "mean       12.183273  2011-07-10 12:08:23.848567552       3.474064   \n",
       "min    -80995.000000            2010-12-01 08:26:00       0.000000   \n",
       "25%         2.000000            2011-04-06 15:02:00       1.250000   \n",
       "50%         5.000000            2011-07-29 15:40:00       1.950000   \n",
       "75%        12.000000            2011-10-20 11:58:30       3.750000   \n",
       "max     80995.000000            2011-12-09 12:50:00   38970.000000   \n",
       "std       250.283037                            NaN      69.764035   \n",
       "\n",
       "          CustomerID  \n",
       "count  401604.000000  \n",
       "mean    15281.160818  \n",
       "min     12346.000000  \n",
       "25%     13939.000000  \n",
       "50%     15145.000000  \n",
       "75%     16784.000000  \n",
       "max     18287.000000  \n",
       "std      1714.006089  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understanding the overall statistis of the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87c9f87c-dea6-49c0-aca0-5093b2192470",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dfc55f2-5605-4f46-b611-2330e78a5e0e",
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   "outputs": [
    {
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       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
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      "text/plain": [
       "  InvoiceNo StockCode                         Description  Quantity  \\\n",
       "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  "
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "df.head(1)"
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   "execution_count": 14,
   "id": "f8e40662-a514-450c-a947-0e5d1bf029c1",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                         Description  Quantity  \\\n",
       "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                 WHITE METAL LANTERN         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country        day  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Wednesday   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Wednesday   \n",
       "\n",
       "      month  year  \n",
       "0  December  2010  \n",
       "1  December  2010  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day'] = df.InvoiceDate.dt.day_name()\n",
    "df['month'] = df.InvoiceDate.dt.month_name()\n",
    "df['year'] = df.InvoiceDate.dt.year\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46418522-5298-4dd4-a658-d7e3f2e00f30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53fc0006-049d-45b3-903f-674178387527",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "10763395-74c3-4fbe-b551-68cf31b285c1",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ffbc9f7-b61a-4ba1-b7f9-e9270944074f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>December</td>\n",
       "      <td>2010</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country        day  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  Wednesday   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Wednesday   \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  Wednesday   \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Wednesday   \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  Wednesday   \n",
       "\n",
       "      month  year  total_price  \n",
       "0  December  2010        15.30  \n",
       "1  December  2010        20.34  \n",
       "2  December  2010        22.00  \n",
       "3  December  2010        20.34  \n",
       "4  December  2010        20.34  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating total price column for each of the orders\n",
    "\n",
    "df['total_price'] = df['Quantity'] * df['UnitPrice']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "db5d57f6-89eb-47fd-97ae-e040e6a07d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>325</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>18</td>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>309</td>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            InvoiceDate  InvoiceNo  total_price\n",
       "CustomerID                                     \n",
       "12346.0             325          2         0.00\n",
       "12347.0               1        182      4310.00\n",
       "12348.0              74         31      1797.24\n",
       "12349.0              18         73      1757.55\n",
       "12350.0             309         17       334.40"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a dataframef for total price, customer frequency and recency\n",
    "\n",
    "customer_df = df.groupby('CustomerID').agg({'InvoiceDate': lambda x: (df['InvoiceDate'].max() - x.max()).days,\n",
    "                                            'InvoiceNo': 'count',\n",
    "                                            'total_price': 'sum'})\n",
    "customer_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f7b4d48-270e-4306-8d33-3c0491a9e9be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4372 entries, 12346.0 to 18287.0\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   recency    4372 non-null   int64  \n",
      " 1   frequency  4372 non-null   int64  \n",
      " 2   monetary   4372 non-null   float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 136.6 KB\n"
     ]
    }
   ],
   "source": [
    "# renaming the columns of customer_df DataFrame\n",
    "customer_df.rename(columns={'InvoiceDate': 'recency',\n",
    "                            'InvoiceNo': 'frequency',\n",
    "                            'total_price': 'monetary'}, inplace=True)\n",
    "\n",
    "customer_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f50bf8-d3a7-4412-a035-a50221c8cb89",
   "metadata": {},
   "source": [
    "The above dimension used are;\n",
    "\n",
    "    Recency – How recently did the customer purchase?\n",
    "    Frequency – How often do they purchase?\n",
    "    Monetary Value – How much do they spend?\n",
    "    \n",
    "Which is known as the RFM Model\n",
    "\n",
    "In this analysis, we must be careful with the time window since it can be biased or incurate if we\n",
    "e try to span an extremely long duration.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412cd136-38d4-4bd3-ac07-b4188c12a8b2",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c2b6d78-128c-43cc-a3c3-996efc810523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(customer_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a397349-07cb-4206-90e5-fb815f352103",
   "metadata": {},
   "source": [
    "Analysis was carried out to better understand the relationship between the  Recency, Frequency and Monetary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9973e3af-467c-42a3-8efa-db286ce3030e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4e56b0ae-01ec-47d6-b074-f62abbb9fe66",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765823aa-9731-4f18-b94e-5744fb57af9d",
   "metadata": {},
   "source": [
    "Using StandardScaler to ensures that all the variables are on the same scale, which is necessary for K-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fa539d5-93fa-41ac-b2fd-6c730a6c0ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.71313673e-01 1.28024581e-04 1.51091712e-02]\n",
      " [2.68096515e-03 2.31724491e-02 3.02971721e-02]\n",
      " [1.98391421e-01 3.84073742e-03 2.14424619e-02]\n",
      " ...\n",
      " [1.87667560e-02 1.53629497e-03 1.57314917e-02]\n",
      " [8.04289544e-03 9.21776981e-02 2.23174105e-02]\n",
      " [1.12600536e-01 8.83369607e-03 2.15835588e-02]]\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_scaled = scaler.fit_transform(customer_df)\n",
    "print(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "05f94343-d630-4bcf-b095-a093f59be481",
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding the wcss value for different number of clusters\n",
    "\n",
    "wcss = []\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters = i, random_state = 42)\n",
    "    kmeans.fit(X_scaled)\n",
    "\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f90d00d3-8cd8-4623-bfa3-5f714947e7dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot an elbow graph\n",
    "\n",
    "sns.set()\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('The Elbow Point Graph')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d98234-eafb-4d60-92c1-2a5f34069ac1",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "There is a rapid increase of performance when n_cluster went from 1 to 2, yet after 3 clsuters, the performance maitained roughly at the same level. We can choose between 2 and 3 clusters and here we keep the n_clsuter at 3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2d71d4f-64d3-4609-8b13-6823432c3ab0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7bc13098-fa90-4b36-b9ff-2b94a68c15e5",
   "metadata": {},
   "source": [
    "## Training the K-Means Clustering Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e23887f-9241-4bdb-8e09-1da722493b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, n_init=10, random_state=33)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=3, n_init=10, random_state=33)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, n_init=10, random_state=33)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, n_init=10, random_state=33)\n",
    "kmeans.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a4cae37-f234-4cf8-b26c-302e1381a648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>12346.0</th>\n",
       "      <td>0.871314</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.015109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>0.002681</td>\n",
       "      <td>0.023172</td>\n",
       "      <td>0.030297</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>0.198391</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>0.021442</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>0.048257</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>0.021303</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>0.828418</td>\n",
       "      <td>0.002048</td>\n",
       "      <td>0.016288</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18280.0</th>\n",
       "      <td>0.742627</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.015746</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18281.0</th>\n",
       "      <td>0.482574</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.015394</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18282.0</th>\n",
       "      <td>0.018767</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>0.015731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.0</th>\n",
       "      <td>0.008043</td>\n",
       "      <td>0.092178</td>\n",
       "      <td>0.022317</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.0</th>\n",
       "      <td>0.112601</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>0.021584</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4372 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary  cluster\n",
       "CustomerID                                        \n",
       "12346.0     0.871314   0.000128  0.015109        1\n",
       "12347.0     0.002681   0.023172  0.030297        2\n",
       "12348.0     0.198391   0.003841  0.021442        2\n",
       "12349.0     0.048257   0.009218  0.021303        2\n",
       "12350.0     0.828418   0.002048  0.016288        1\n",
       "...              ...        ...       ...      ...\n",
       "18280.0     0.742627   0.001152  0.015746        1\n",
       "18281.0     0.482574   0.000768  0.015394        0\n",
       "18282.0     0.018767   0.001536  0.015731        2\n",
       "18283.0     0.008043   0.092178  0.022317        2\n",
       "18287.0     0.112601   0.008834  0.021584        2\n",
       "\n",
       "[4372 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.DataFrame(X_scaled, columns=customer_df.columns, index=customer_df.index)\n",
    "\n",
    "final_df['cluster'] = kmeans.labels_\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5b94c15-e91c-429e-baf2-c216efb02267",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "db67a0de-beb7-4d66-8ba1-c722fabc53f7",
   "metadata": {},
   "source": [
    "## Characteristics Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54028aa-d123-44d0-a7fd-4c638f8a19ae",
   "metadata": {},
   "source": [
    "Now let's have a look at the characteristics of each cluster, and see what customer insight we can obtain and try to answer our questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9831d107-be52-4191-87ad-264b603929b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_mean = final_df.groupby('cluster')[['recency', 'frequency', 'monetary']].mean().reset_index()\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3, figsize=(4, 6))\n",
    "\n",
    "sns.barplot(cluster_mean, x='cluster', y='recency', ax=axes[0])\n",
    "sns.barplot(cluster_mean, x='cluster', y='frequency', ax=axes[1])\n",
    "sns.barplot(cluster_mean, x='cluster', y='monetary', ax=axes[2])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5bd31dc0-834a-4fbe-bd7b-119083627ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "2    2922\n",
       "0     816\n",
       "1     634\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d437aff-fa98-4717-95f6-abe5474b83ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9071bf5-c751-4c63-bf24-1a05a40445d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fe1bcc93-190e-4448-9cce-6861ca077eb2",
   "metadata": {},
   "source": [
    "## Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "616c0c04-1e51-4b0e-93f1-11f288309eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "925026be-09a2-472f-8f28-609b05589726",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"customer_segmentation.pki\", \"wb\") as f:\n",
    "    pickle.dump(kmeans,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594718dc-426e-48ed-ad0e-eab56f4e55c0",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c6f87a-0e7a-4dc5-bc99-d1add0ed8f0e",
   "metadata": {},
   "source": [
    "After segmenting the customer data into different custers, we arrived at a unique characteristic and behavioural pattern exhited by each group. The segmentation revealed clear patterns in terms of customer recency, purchase frequency, and monetary value.\n",
    "\n",
    "Cluster 0: \"High-Value Regular Customers\":\n",
    "This cluster consists of customers with relatively high monetary value, moderate frequency, and recent transactions. They are likely to be loyal and valuable customers who make regular purchases.\n",
    "\n",
    "Cluster 1: \"Low-Value Occasional Customers\":\n",
    "This cluster includes customers with lower monetary value, lower frequency, and higher recency. They might be occasional buyers who make infrequent purchases.\n",
    "\n",
    "Cluster 2: \"High-Value VIP Customers\":\n",
    "This cluster represents customers with exceptionally high monetary value, high frequency, and very low recent transactions. They are top-tier customers who contribute significantly to the business's revenue and should be treated as VIPs.\n",
    "\n",
    "Based on this analysis, we can help retention team to analyze churn customers before they leave the company and reducing customer churn to the avegerage of 5-10% annually.\n",
    "\n",
    "With this, we can perfect strategic marketing campaigns by 20% and increased companies revneue by 15%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c3fb032-50b3-41e8-844c-7299eb99c75b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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